Since some content in video is often moving, extracting non-blurry, high-quality frames to use as still photos can be a challenging task. Although certain existing solutions automate the process of searching for quality frames, which is tedious or infeasible to perform manually, these solutions present disadvantages. For instance, existing blur detection solutions are designed to detect and exclude video frames that are completely or nearly completely blurry. However, many videos have frames where a majority of the content in the frame is clear, but part of the content in the frame exhibits motion blur. In such frames, stationary content is clear, but moving content is blurry. As examples, the moving content may be a ball being thrown, a hand or a foot, any of which can move relatively quickly compared to other moving content (e.g., active people in the frame). Such frames may not be detected using existing blur detection algorithms. Thus, when using blur detection to identify the clearest frames for use as stills and to exclude frames with motion blur, existing techniques cause false positives to be included in the final result.